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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2406.14503 |
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| _version_ | 1866910495821266944 |
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| author | Liang, Jingcong Wang, Junlong Zhai, Xinyu Zhuang, Yungui Zheng, Yiyang Xu, Xin Ran, Xiandong Dong, Xiaozheng Rong, Honghui Liu, Yanlun Chen, Hao Wei, Yuhan Li, Donghai Peng, Jiajie Huang, Xuanjing Shi, Chongde Feng, Yansong Song, Yun Wei, Zhongyu |
| author_facet | Liang, Jingcong Wang, Junlong Zhai, Xinyu Zhuang, Yungui Zheng, Yiyang Xu, Xin Ran, Xiandong Dong, Xiaozheng Rong, Honghui Liu, Yanlun Chen, Hao Wei, Yuhan Li, Donghai Peng, Jiajie Huang, Xuanjing Shi, Chongde Feng, Yansong Song, Yun Wei, Zhongyu |
| contents | We give a detailed overview of the CAIL 2023 Argument Mining Track, one of the Chinese AI and Law Challenge (CAIL) 2023 tracks. The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. It mainly uses summarized judgment documents but can also refer to trial recordings. The track consists of two stages, and we introduce the tasks designed for each stage; we also extend the data from previous events into a new dataset -- CAIL2023-ArgMine -- with annotated new cases from various causes of action. We outline several submissions that achieve the best results, including their methods for different stages. While all submissions rely on language models, they have incorporated strategies that may benefit future work in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14503 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Overview of the CAIL 2023 Argument Mining Track Liang, Jingcong Wang, Junlong Zhai, Xinyu Zhuang, Yungui Zheng, Yiyang Xu, Xin Ran, Xiandong Dong, Xiaozheng Rong, Honghui Liu, Yanlun Chen, Hao Wei, Yuhan Li, Donghai Peng, Jiajie Huang, Xuanjing Shi, Chongde Feng, Yansong Song, Yun Wei, Zhongyu Computation and Language We give a detailed overview of the CAIL 2023 Argument Mining Track, one of the Chinese AI and Law Challenge (CAIL) 2023 tracks. The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. It mainly uses summarized judgment documents but can also refer to trial recordings. The track consists of two stages, and we introduce the tasks designed for each stage; we also extend the data from previous events into a new dataset -- CAIL2023-ArgMine -- with annotated new cases from various causes of action. We outline several submissions that achieve the best results, including their methods for different stages. While all submissions rely on language models, they have incorporated strategies that may benefit future work in this field. |
| title | Overview of the CAIL 2023 Argument Mining Track |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.14503 |